Abstract：RAMPthe Repository Analytics and Metrics PortalA prototype web service that accurately counts item downloads from institutional repositories Purpose The purpose of this paper is to present data that begin to detail the deficiencies of log file analytics reporting methods that are commonly built into institutional repositoryIRplatformsThe authors propose a new method for collecting and reporting IR item download metricsThis paper introduces a web service prototype that captures activity that current analytics methods are likely to either miss or over-reportDesignmethodologyapproach Data were extracted from DSpace Solr logs of an IR and were cross-referenced with Google Analytics and Google Search Console data to directly compare Citable Content Downloads recorded by each methodFindings This study provides evidence that log file analytics data appear to grossly over-report due to traffic from robots that are difficult to identify and screenThe study also introduces a proof-of-concept prototype that makes the research method easily accessible to IR managers who seek accurate counts of Citable Content DownloadsResearch limitationsimplications The method described in this paper does not account for direct access to Citable Content Downloads that originate outside Google Search propertiesOriginalityvalue This paper proposes that IR managers adopt a new reporting framework that classifies IR page views and download activity into three categories that communicate metrics about user activity related to the research processIt also proposes that IR managers rely on a hybrid of existing Google Services to improve reporting of Citable Content Downloads and offers a prototype web service where IR managers can test results for their repositories Instificational CollectionEliminate the data report of the institutional collection project through a more accurate download measureThe analysis of the log file of this study is to extract and compare different sources of dataThe purpose is to more accurately understand the download of the institutional collection projectThis involves extravagant information from a large amount of datawhich is the core concept of data exploration